Cancer remains the leading cause of death in the world despite improvements in early diagnosis and therapy. The development of new classes of drugs and the growing insight into the cellular processes involved in malignant growth has led to renewed enthusiasm to treat various forms of cancer. The absence of clear results is in part due to the fact that no validated biological markers (or biomarkers) currently exist for measuring and predicting the effect of therapy.
Tumor response as measured by anatomic imaging, which was used to measure therapeutic efficacy in the era of cytotoxic drugs, has been a valuable tool in validating clinical trials. Advanced imaging technologies, including both metabolic and molecular imaging platforms, are becoming important partners in evaluating response as a clinically meaningful study endpoint. Tumor response is typically determined by measurements of changes in tumor burden, most often obtained from computed tomography (CT) or magnetic resonance imaging (MRI). The tumor burden size is most often computed based on morphological features such as the longest axial diameter in one-dimensional Response Evaluation Criteria in Solid Tumors (RECIST) or the two-dimensional World Health Organization (WHO) criterion which are commonly used for assessing response to treatment, to define clinical trial endpoints, and are useful in studies evaluating cytotoxic drugs in which tumor shrinkage is expected. Together with the developing of cytostatic drugs resulting in little or no tumor shrinkage, there is a need for biomarkers of response which do not depend on morphological features.
Attempts have been at improving the measure of response criteria by incorporating intensity related features such as tumor density and size into a single index. An improved response evaluation criterion was defined as a greater than 10% decrease in tumor size or a greater than 15% decrease in tumor density. The problem with such methods is that rely on prior identification and segmentation of lesions which is highly process and operator dependent. The accuracy of quantitative biomarker assessment is limited by the accuracy of the segmentation method used to define the lesions of interest. There is therefore a need for biomarker assessment methods which do not depend on segmentation of lesions.
Furthermore, registration and delineation of lesions are generally performed based on 2D slices limiting the spatial relationship measures between the pixels in a tumor, hence the texture and size feature measurements are not as accurate as they would be in a 3D model. Therefore, there is a need for quantitative methods of imaging biomarker computation able to overcome the limitations of current 2D image processing techniques.
Three-dimensional solid texture analysis has been widely applied in various fields of imaging. U.S. Pat. No. 8,457,414 to Jahanbin et al. discloses a method for detecting textural defects in an image using second-order statistical attributes extracted from a GLCM.
Texture analysis is also a promising field in biomedical imaging. It is found that most of the tissue types have strong multi-scale directional properties that are well captured by imaging protocols with high resolutions and spherical spatial transfer functions. (Depeursinge, 2013). Texture in medical images is defined as cellularly organized areas of pixels. Such patterns can be described by a given spatial organization of grey levels (e.g., random, periodic). Early examples of texture features are the autocorrelation function, textural edginess, measurements derived from mathematical morphology, run-length and gray-level co-occurrence matrices (GLCMs).
The majority of papers describing computer-aided diagnosis for lung diseases use texture features to classify lung tissue patterns. Aisen et al. describe a content-based image retrieval system in which the physician delineates a suspicious region of interest (ROI). The system then matches the ROI against reference ROIs in images that are already indexed in the database through co-occurrence matrices, grey-level distributions and size of the ROI. Markel et al., proposes the use of 3D texture features for automated segmentation of lung carcinoma
While most existing cancer biomarkers have been limited to measures of morphological changes, many of the new treatments do not translate into measurable changes in size of tumors. There is therefore a need for novel quantitative biomarkers of disease progression which can measure changes in texture over a period of time.
Additionally, current imaging biomarkers typically requires the segmentation of ROIs. Image segmentation is often subjective and lacks repeatability resulting in reduced accuracy of the biomarkers. There is therefore a need for biomarkers which can be computed without the need for prior image segmentation.